Often real world data are contaminated with a small amount of noise and these noises can substantially change the clusters that we obtain using the underlying algorithm. To circumvent the issue created by such noises, there are several studies of clustering problems with outliers, see for instance, [1]}, [2]}, [3]}, [4]}, [5]}, [6]}.
